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In this paper, a new neural network model inspired by the biological immune system functions is presented. The model, termed Artificial Immune-Activated Neural Network (AIANN), extracts classification knowledge from a training data set, which is then used to classify input patterns or vectors. The AIANN is based on a neuron activation function whose behavior is conceptually modeled after the chemical bonds between the receptors and epitopes in the biological immune system. The bonding is controlled through an energy measure to ensure accurate recognition. The AIANN model was applied to the segmentation of 3-dimensional magnetic resonance imaging (MRI) data of the brain and a contextual basis was developed for the segmentation problem. Evaluation of the segmentation results was performed using both real MRI data obtained from the Center for Morphometric Analysis at Massachusetts General Hospital and simulated MRI data generated using the McGill University BrainWeb MRI simulator. Experimental results demonstrated that the AIANN model attained higher average results than those obtained using published methods for real MRI data and simulated MRI data, especially at low levels of noise.

Total organic carbon (TOC) is the amount of carbon present in an organic compound and is often used as an essential factor for unconventional shale resources evaluation. The previous models for TOC determination were either based on density log data only and considered the presence of organic matter is proportional to the bulk density, or based on resistivity log, sonic or density logs as well as the formation level of maturity (LOM), where these models assumed a linear relation between resistivity and porosity logs. The average absolute deviation (ADD) of the previous model was not less than 1.20wt% of TOC with a coefficient of determination (R2) of less than 0.85. The objective of this research is to develop new empirical correlation to determine the TOC based on well logs using artificial neural network for Barnett shale formation. Core TOC data (442 data point) and well logs (resistivity, gamma ray, sonic transit time, and bulk density) were used to develop the ANN model. For the first time, the ANN model will change to a white box by extracting the weights and biases of the model to form the empirical equation. The results obtained showed that TOC is strong function of bulk density, and moderate function of gamma ray, compressional sonic time, and week function of deep resistivity. The developed ANN model is able to predict the TOC based on conventional log data with high accuracy (the ADD is 0.91wt% of TOC and R2 between estimated and actual TOC is 0.93). The developed empirical equation for TOC determination from the ANN model outperformed the previous available models, which had an ADD of 1.20 wt% or more and R2 of less than 0.85. The developed TOC model and equation can be applied using simple computer without the need for a specific software. The novelty of this new research is the simplicity and high accuracy of the developed model for estimating the total organic carbon based on conventional log data. The developed empirical equation will help the geologists and reservoir engineers to predict the TOC without the need for hard lab work or complicated softwares.

In this article we describe a statistical model that was developed to segment brain magnetic resonance images. The statistical segmentation algorithm was applied after a pre-processing stage involving the use of a 3D anisotropic filter along with histogram equalization techniques. The segmentation algorithm makes use of prior knowledge and a probabilitybased multivariate model designed to semi-automate the process of segmentation. The algorithm was applied to images obtained from the Center for Morphometric Analysis at Massachusetts General Hospital as part of the Internet Brain Segmentation Repository (IBSR). The developed algorithm showed improved accuracy over the k-means, adaptive Maximum Apriori Probability (MAP), biased MAP, and other algorithms. Experimental results showing the segmentation and the results of comparisons with other algorithms are provided. Results are based on an overlap criterion against expertly segmented images from the IBSR. The algorithm produced average results of approximately 80% overlap with the expertly segmented images (compared with 85% for manual segmentation and 55% for other algorithms).